Collaborative Research: HCC: MEDIUM: Body as Intervention: Toward Closed-Loop, Embodied Behavioral Health Interventions

在美国，压力和焦虑急剧增加，导致了精神疾病的流行现在比以往任何时候都更迫切需要有效的心理健康干预。通过监测用户的症状及其背景（例如，当有人焦虑发作或经过酒吧时经历渴望）通过可穿戴设备和IoT（物联网）设备，移动的健康（mHealth）技术有可能改变心理健康护理。尽管有先进的监测能力，但大多数现有的移动保健干预措施都是传统保健干预措施的数字化，无法根据用户的症状提供即时的精确干预措施。因此，它们继承了前人的局限性：依赖人的动机，需要积极参与才能有效，导致遵守程度有限。为了解决这个问题，研究人员将开发一类新的解决方案-感官干预-可以有效地不干扰用户或需要他们的积极参与。感官干预是实时闭环系统，其响应于用户的行为或生理信号而直接作用于用户的身体或直接环境。与现有的解决方案不同，感官干预联合收割机结合了应用工程、信号处理和机器学习，无需用户努力即可自动触发干预。该项目将创建三种类型的闭环可穿戴和物联网系统，使用不同的模式（振动，气流和触摸）在心理健康环境中提供感官干预，例如渴望，工作场所压力和社会压力。最终，该项目将使移动健康干预措施与移动健康监测解决方案一样丰富，多样化和个性化。该项目将产生开源软件，硬件设计和数据集。与康奈尔科技精准健康计划和芝加哥大学医学及其临床和行业合作伙伴的合作将通过临床评估和商业化加速研究的传播。大多数现有的mHealth行为健康干预措施，虽然加上先进的传感系统来检测健康需求，但需要有意识的信息认知处理和用户的积极参与才能有效。该项目将介绍和发展感官干预的概念，这是一种新型的移动健康干预措施，只需很少或根本不需要认知意识即可有效。该项目将分四个阶段研究感官干预：（一）调查和规划外部援助的方式;（ii）设计和开发能够在mHealth的约束内实现这些感官干预的设备，（iii）确定与目标行为相关联的生理信号，并集成传感系统，信号处理，和机器学习与感官干预，以实现闭环系统，自动触发干预，和（iv）评估的有效性，可用性和可接受性的闭环系统（在实验室和现场）。在整个过程中，研究人员将评估和描述感官干预如何影响三种常见的压力引起的心理健康挑战：物质渴望，工作压力和社会压力。为了干预对物质的渴望，研究人员将利用心率生物反馈，开发一种基于智能手表的系统，使用振动触觉器提供生物反馈，并评估这种振动触觉驱动如何减轻对酒精和尼古丁的渴望。为了干预工作场所的压力，研究人员将利用呼吸法规，开发一种基于风扇的系统，改变鼻子周围气流的感知，并评估这种气流如何在工作场所进行缓慢的引导呼吸。为了干预社会压力，研究人员将利用情感触摸，开发一种激活情感触摸神经元的手臂佩戴设备，并评估情感触摸如何帮助调节社会压力。总的来说，这项研究将使一类新的移动健康干预措施，是响应用户的健康情况下，在实时和可以有效的用户的认知能力或availability.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

There has been a drastic increase in stress and anxiety in the U.S., leading to a mental health pandemic. The need for effective mental health interventions is more urgent now than ever. By monitoring users' symptoms and their context (e.g., when someone is having an anxiety attack or experiencing cravings when passing by a bar) through wearables and IoT (Internet of Things) devices, mobile health (mHealth) technologies have the potential to transform mental health care. Despite the advanced monitoring capability, most existing mHealth interventions are digitization of traditional health interventions that do not deliver in-the-moment precision interventions in response to users' symptoms. As such, they inherit the limitations of their predecessors: the reliance on human motivation and the need for active engagement to be effective, resulting in limited adherence. To address this problem, the investigators will develop a class of novel solutions – sensory interventions – that can be effective without disrupting the users or requiring their active engagement. Sensory interventions are real-time closed-loop systems that directly act on the users’ bodies or immediate environment in response to users behavioral or physiological signals. Unlike existing solutions, sensory interventions combine applied engineering, signal processing, and machine learning to trigger interventions autonomously without user effort. The project will create three types of closed-loop wearable and IoT systems that use different modalities (vibration, airflow, and touch) to deliver sensory interventions in mental health contexts, such as cravings, workplace stress, and social stress. Ultimately, this project will enable mHealth interventions to be as rich, diverse, and personalized as mHealth monitoring solutions. This project will produce open-source software, hardware designs, and datasets. Collaborations with Cornell Tech Precision Health Initiative and with the University of Chicago Medicine and their clinical and industry partners will accelerate the dissemination of research through clinical evaluations and commercialization. Most existing mHealth behavioral health interventions, although coupled with advanced sensing systems to detect health needs, require conscious cognitive processing of information and active participation from users to be effective. This project will introduce and develop the concept of sensory interventions, a novel class of mHealth interventions that require little or no cognitive awareness to be effective. This project will investigate sensory interventions in four stages: (i) investigate and map modalities of external (electromechanical) stimuli to actuate neurological responses that produce a neurophysiological effect (ii) design and develop devices that enable these sensory interventions within the constraints of mHealth, (iii) determine physiological signals that are associated with target behaviors and integrate sensing systems, signal processing, and machine learning with sensory interventions to achieve closed-loop systems that automatically triggers intervention, and (iv) evaluate the efficacy, usability, and acceptability of the closed-loop systems (both in-lab and in situ). Throughout this process, the investigators will evaluate and characterize how sensory interventions impact three common stress-induced mental health challenges: substance cravings, workplace stress, and social stress. To intervene in substance cravings, the investigators will leverage heart rate biofeedback, develop a smartwatch-based system to deliver biofeedback using vibrotactors, and evaluate how such vibrotactile actuation mitigates alcohol and nicotine cravings. To intervene in workplace stress, the investigators will leverage breathing regulations, develop a fan-based system that alters the perception of airflow around the nose, and evaluate how such airflow entrains slow, guided breathing in the workplace. To intervene in social stress, the investigators will leverage affective touch, develop an arm-worn device that activates affective touch neurons, and evaluate how affective touch helps regulate social stress. Collectively, this research will enable a new class of mHealth interventions that are responsive to users’ health context in real-time and can be effective irrespective of users cognitive capacity or availability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.